Mustererkennung (Pattern Recognition)

Bayesian decision rule
Performance assessment in pattern recognition
Supervised learning with parametric densities
Supervised learning with non-parametric densities, classification
Linear decision functions, single-layer perceptron
Support vector machines (SVMs)
Multi-layer perceptron, neural networks (NNs)
Boosting methods
Non-supervised learning, clustering

Lecturer: Prof. Tim Fingscheidt
Assitant: Björn Möller

Lecture (ET-NT-102):
Contact hours (SWS): 2 h
Time: Monday 13:15 - 14:45 pm (first lecture on monday xx.xx.2025)
Location: SN 22.1
Language: German
StudIP Link

Seminar (ET-NT-103):
Contact hours (SWS): 2 h (held as block seminar)
Date: xx.xx. and xx.xx.2025
Time: 9.30 am - 6:00 pm
Location: SN 22.1
Language: German

StudIP Link

Mustererkennung (Pattern Recognition)

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